Research Papers

Effect of Condition Monitoring on Risk Mitigation for Steam Turbines in the Forest Products Industry

[+] Author and Article Information
Bin Zhou

Risk, Reliability and Failure Prevention Area,
FM Global Research,
1151 Boston-Providence Turnpike,
Norwood, MA 02062
e-mail: bin.zhou@fmglobal.com

Kumar Bhimavarapu

Risk, Reliability and Failure Prevention Area,
FM Global Research,
1151 Boston-Providence Turnpike,
Norwood, MA 02062
e-mail: kumar.bhimavarapu@fmglobal.com

1Corresponding author.

Manuscript received January 28, 2016; final manuscript received January 4, 2017; published online June 12, 2017. Assoc. Editor: Jeremy M. Gernand.

ASME J. Risk Uncertainty Part B 3(3), 031003 (Jun 12, 2017) (8 pages) Paper No: RISK-16-1009; doi: 10.1115/1.4035704 History: Received January 28, 2016; Revised January 04, 2017

Industry has been implementing condition monitoring (CM) for turbines to minimize losses and to improve productivity. Deficient conditions can be identified before losses occur by monitoring the equipment parameters. For any loss scenario, the effectiveness of monitoring depends on the stage of the loss scenario when the deficient condition is detected. A scenario-based semi-empirical methodology was developed to assess various types of condition monitoring techniques, by considering their effect on the risk associated with mechanical breakdown of steam turbines in the forest products (FPs) industry. A list of typical turbine loss scenarios was first generated by reviewing loss data and leveraging expert domain knowledge. Subsequently, condition monitoring techniques that can mitigate the risk associated with each loss scenario were identified. For each loss scenario, an event tree analysis (ETA) was used to quantitatively assess the variations in the outcomes due to condition monitoring, and resultant changes in the risk associated with turbine mechanical breakdown. An application was developed following the methodology to evaluate the effect of condition monitoring on turbine risk mitigation.

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Fig. 1

A schematic of loss scenario progression and CM

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Fig. 2

A schematic event tree for risk evaluation with CM

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Fig. 3

Share of loss value for groups of loss scenarios

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Fig. 4

Share of loss count for groups of loss scenarios

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Fig. 5

Severity and likelihood of loss scenarios for FP steam turbines

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Fig. 6

Risk ratios for all loss scenarios

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Fig. 7

Comparison of current and past risk levels for all loss scenarios




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